• Communications for Statistical Applications and Methods
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• 제16권4호
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• pp.675-686
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• 2009

In this paper, it will be assumed that there are two distinct populations which are multivariate normal with equal covariance matrix. We also assume that the two populations are equally likely and the costs of misclassification are equal. The classification rule depends on the situation when the training samples include missing values or not. We consider the bootstrap confidence intervals for classification error rate when a block of observation is missing.

• 한국지하수토양환경학회:학술대회논문집
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• 한국지하수토양환경학회 2000년도 추계학술대회
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• pp.15-22
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• 2000

Long ranged groundwater-level data often have the missing intervals because of the trouble of monitoring systems at the national groundwater monitoring wells. Geostatistical methods are very useful for the supplement of the missing data. Ordinary kriging was applied for the interpolation of the missing groundwater-level data with a smooth sinusoidal variation. Conditional simulation was used for the reproduction of the missing data with high fluctuations. Two geostatistical methods produced the very accurate estimates at the missing intervals and reproduced their original variations. This fact is proved by the cross validation test and graphical method, respectively.

• Journal of the Korean Data and Information Science Society
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• 제20권4호
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• pp.757-764
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• 2009

In discriminant analysis, we consider a special pattern which contains a block of missing observations. We assume that the two populations are equally likely and the costs of misclassification are equal. In this situation, we consider the bootstrap confidence intervals of the error rate in the circular models when the covariance matrices are equal and not equal.

• Journal of the Korean Data and Information Science Society
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• 제24권1호
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• pp.189-200
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• 2013

In this paper, it will be assumed that there are two distinct populations which are multivariate normal with equal covariance matrix. We also assume that the two populations are equally likely and the costs of misclassification are equal. The classification rule depends on the situation whether the training samples include missing values or not. We consider the conditional bootstrap confidence intervals for classification error rate when a block of observation is missing.

• Journal of the Korean Data and Information Science Society
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• 제23권6호
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• pp.1271-1277
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• 2012

In this paper, we propose an estimation method of the parameter in an exponential distribution based on a progressive Type I interval censored sample with semi-missing observation. The maximum likelihood estimator (MLE) of the parameter in the exponential distribution cannot be obtained explicitly because the intervals are not equal in length under the progressive Type I interval censored sample with semi-missing data. To obtain the MLE of the parameter for the sampling scheme, we propose a method by which progressive Type I interval censored sample with semi-missing data is converted to the progressive Type II interval censored sample. Consequently, the estimation procedures in the progressive Type II interval censored sample can be applied and we obtain the MLE of the parameter and survival function. It will be shown that the obtained estimators have good performance in terms of the mean square error (MSE) and mean integrated square error (MISE).

• 한국해안해양공학회지
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• 제19권1호
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• pp.45-56
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• 2007

수량 및 수질자료, 특히 수질자료가 없는 구간에서의 직접계산에 의한 오염부하 산정은 불가능하기 때문에 적절한 방법을 이용하여 결측구간의 자료를 보완(data filling)하여 계산하는 추정과정을 필요로 한다. 본 연구에서는 수질자료가 없는 구간, 즉 수질 결측구간에서 오염부하량을 산정하기 위한 다양한 농도 추정방법을 제시하고, 제시된 방법을 이용하여 추정된 농도변화 양상 분석 및 오염부하 변동양상을 비교 분석하여 보다 효과적이고, 효율적인 추정방법을 최종 제안하였다. 또한, 오염부하에 영향을 미치는 수량 및 수질인자의 상대적인 중요성과 연안 하천의 오염부하 특성을 구분할 수 있는 영향인자를 제시하였다. 수질자료 결측구간의 다양한 농도 추정방법을 이용하여 한강하구의 오염부하를 산정한 결과, 결측구간을 제외하고 추정한 오염부하는 매우 낮은 비현실적인 결과를 제시하였으며, 가용자료의 변동성을 고려한 선형내삽법이 가장 적합한 방법으로 파악되었다. 또한, 한강하구의 오염부하양상은 수량주도형으로 판단되었으며, 결측구간의 농도추정은 불가피한 과정으로 적절한 추정방법을 이용하는 것이 보다 바람직한 것으로 파악되었다.

• Communications for Statistical Applications and Methods
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• 제11권2호
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• pp.235-246
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• 2004

• 한국멀티미디어학회논문지
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• 제24권9호
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• pp.1251-1260
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• 2021

This paper discusses missing data processing using simple moving average (SMA) and kalman filter. Also SMA and kalman predictive value are made a comparative study. Time series analysis is a generally method to deals with time series data in photovoltaic field. Photovoltaic system records data irregularly whenever the power value changes. Irregularly recorded data must be transferred into a consistent format to get accurate results. Missing data results from the process having same intervals. For the reason, it was imputed using SMA and kalman filter. The kalman filter has better performance to observed data than SMA. SMA graph is stepped line graph and kalman filter graph is a smoothing line graph. MAPE of SMA prediction is 0.00737%, MAPE of kalman prediction is 0.00078%. But time complexity of SMA is O(N) and time complexity of kalman filter is O(D²) about D-dimensional object. Accordingly we suggest that you pick the best way considering computational power.

• 대한전기학회논문지:시스템및제어부문D
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• 제52권6호
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• pp.372-377
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• 2003

Many of modem radars use modulated pulse repetition intervals for the purpose of anti-aliasing and ECCM. The interception, analysis and identification of radar signals is a major function of a radar intercept receiver. In this paper, we discuss the identification of pulse repetition intervals modulation of radar signals which is one of the major parameters for the analysis of radar. We proposed a new algorithm based on Markov models approach. This approach is shown to be reliable and robust to the missing pulses, as well as to require only relatively few pulse data.

• 한국해안·해양공학회논문집
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• 제33권6호
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• pp.333-344
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• 2021

해양모니터링 자료에서 빈번하게 발생하는 장기결측구간의 자료 보충기법을 제안한다. 제안하는 방법은 결측구간의 장기변동 추세 성분과 단기변동 잔차성분을 추정하여 조합하는 방식으로 결측구간의 미지 정보를 추정한다. 이 방법을 이용하여 울릉도 해상부이 자료의 수온 항목, 약 1개월 정도의 장기결측 구간의 자료를 보충하였으며, 부이에서 관측하는 자료 항목에 대해서도 결측 보충을 수행하였다. 보충된 자료는 항목에 따라 차이를 보이지만 변동양상이 적절하게 재현되는 것으로 파악되었다. 이 방법은 추세추정과 잔차 반영에 따른 편향오차와 분산오차가 발생하지만, 장기결측으로 인한 통계적인 측도 추정의 편향오차는 크게 절감하는 것으로 파악되었다. 결측보충 모형의 추정 RMS 오차의 평균과 90% 신뢰구간은 각각 0.93, 0.35~1.95 범위이다.